Peptide-Mediated Selective Adhesion of Smooth Muscle and Endothelial Cells in Microfluidic Shear Flow
Bibliographic record
Abstract
Microfluidic devices have recently emerged as effective tools for cell separation compared to traditional techniques. These devices offer the advantages of small sample volumes, low cost, and high purity. Adhesion-based separation of cells from heterogeneous suspensions can be achieved by taking advantage of specific ligand-receptor interactions. The peptide sequences Arg-Glu-Asp-Val (REDV) and Val-Ala-Pro-Gly (VAPG) are known to bind preferentially to endothelial cells (ECs) and smooth muscle cells (SMCs), respectively. This article examines the roles of REDV and VAPG and fluid shear stress in achieving selective capture of ECs and SMCs in microfluidic devices. The adhesion of ECs in REDV-coated devices and SMCs in VAPG-coated devices increases significantly compared to that of the nontargeted cells with decreasing shear stress. Furthermore, the adhesion of these cells is shown to be independent of whether these cells flow through the devices as suspensions of only one cell type or as a heterogeneous suspension containing ECs, SMCs, and fibroblasts. Whereas the overall adhesion of cells in the devices is determined mainly by shear stress, the selectivity of adhesion depends on the type of peptide and on the device surface as well as on the shear stress.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".